Overview

Dataset statistics

Number of variables25
Number of observations4286
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory870.6 KiB
Average record size in memory208.0 B

Variable types

Numeric13
Categorical12

Alerts

cat__ever_married_Yes is highly overall correlated with cat__work_type_children and 1 other fieldsHigh correlation
cat__work_type_Private is highly overall correlated with cat__work_type_Self-employedHigh correlation
cat__work_type_Self-employed is highly overall correlated with cat__work_type_PrivateHigh correlation
cat__work_type_children is highly overall correlated with cat__ever_married_Yes and 1 other fieldsHigh correlation
num__age is highly overall correlated with cat__ever_married_Yes and 1 other fieldsHigh correlation
num__heart_disease is highly imbalanced (66.5%)Imbalance
num__hypertension is highly imbalanced (51.0%)Imbalance
cat__work_type_Never_worked is highly imbalanced (96.6%)Imbalance
num__feat01 has unique valuesUnique
num__feat02 has unique valuesUnique
num__feat03 has unique valuesUnique
num__feat04 has unique valuesUnique
num__feat05 has unique valuesUnique
num__feat06 has unique valuesUnique
num__feat07 has unique valuesUnique
num__feat08 has unique valuesUnique
num__feat09 has unique valuesUnique
num__feat10 has unique valuesUnique

Reproduction

Analysis started2024-03-01 15:15:03.840732
Analysis finished2024-03-01 15:15:14.795356
Duration10.95 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

num__age
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.690979 × 10-16
Minimum-1.9368819
Maximum1.6349641
Zeros0
Zeros (%)0.0%
Negative2024
Negative (%)47.2%
Memory size67.0 KiB
2024-03-01T16:15:14.848656image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-1.9368819
5-th percentile-1.7223619
Q1-0.80672751
median0.087106016
Q30.806533
95-th percentile1.5041592
Maximum1.6349641
Range3.571846
Interquartile range (IQR)1.6132605

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)5.9144241 × 1015
Kurtosis-0.99357792
Mean1.690979 × 10-16
Median Absolute Deviation (MAD)0.76302862
Skewness-0.20139043
Sum6.2527761 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:14.946895image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.46055753 99
 
2.3%
1.504159166 84
 
2.0%
0.5449231878 78
 
1.8%
0.2833133756 76
 
1.8%
0.326915011 74
 
1.7%
0.4141182817 73
 
1.7%
0.6321264585 73
 
1.7%
1.547760801 72
 
1.7%
0.3705166463 72
 
1.7%
0.2397117402 72
 
1.7%
Other values (94) 3513
82.0%
ValueCountFrequency (%)
-1.936881897 2
 
< 0.1%
-1.933393766 3
0.1%
-1.929905635 5
0.1%
-1.926417504 4
0.1%
-1.922929374 2
 
< 0.1%
-1.919441243 3
0.1%
-1.915953112 5
0.1%
-1.912464981 3
0.1%
-1.90897685 4
0.1%
-1.905488719 4
0.1%
ValueCountFrequency (%)
1.634964072 51
1.2%
1.591362436 61
1.4%
1.547760801 72
1.7%
1.504159166 84
2.0%
1.46055753 99
2.3%
1.416955895 39
 
0.9%
1.37335426 50
1.2%
1.329752624 44
1.0%
1.286150989 31
 
0.7%
1.242549354 45
1.0%

num__avg_glucose_level
Real number (ℝ)

Distinct3507
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.0503935 × 10-16
Minimum-1.2591388
Maximum3.450023
Zeros0
Zeros (%)0.0%
Negative2921
Negative (%)68.2%
Memory size67.0 KiB
2024-03-01T16:15:15.028548image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-1.2591388
5-th percentile-1.0010154
Q1-0.65035022
median-0.32282047
Q30.17777108
95-th percentile2.3710983
Maximum3.450023
Range4.7091618
Interquartile range (IQR)0.82812129

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)-3.2786481 × 1015
Kurtosis1.3606351
Mean-3.0503935 × 10-16
Median Absolute Deviation (MAD)0.38982986
Skewness1.5010071
Sum-9.6411767 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:15.118948image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2952502664 5
 
0.1%
-0.7415028657 5
 
0.1%
-0.5042708805 4
 
0.1%
-0.6295122709 4
 
0.1%
-0.2982423816 4
 
0.1%
-0.5243607963 4
 
0.1%
-0.9983973218 4
 
0.1%
-0.3422692185 4
 
0.1%
-0.4677243314 4
 
0.1%
-0.3691982546 4
 
0.1%
Other values (3497) 4244
99.0%
ValueCountFrequency (%)
-1.259138783 1
< 0.1%
-1.24952127 1
< 0.1%
-1.1890378 1
< 0.1%
-1.17877912 1
< 0.1%
-1.123638712 1
< 0.1%
-1.121501487 1
< 0.1%
-1.12086032 1
< 0.1%
-1.120646597 1
< 0.1%
-1.120432875 1
< 0.1%
-1.120219152 1
< 0.1%
ValueCountFrequency (%)
3.450022985 1
< 0.1%
3.426513509 1
< 0.1%
3.420956724 1
< 0.1%
3.417537164 1
< 0.1%
3.39595119 1
< 0.1%
3.33119327 1
< 0.1%
3.32606393 1
< 0.1%
3.27327447 1
< 0.1%
3.251688497 1
< 0.1%
3.185434519 1
< 0.1%

num__bmi
Real number (ℝ)

Distinct409
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.4224134
Maximum8.9302123
Zeros0
Zeros (%)0.0%
Negative2456
Negative (%)57.3%
Memory size67.0 KiB
2024-03-01T16:15:15.206052image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.4224134
5-th percentile-1.4340978
Q1-0.65384858
median-0.10767415
Q30.50352105
95-th percentile1.7649239
Maximum8.9302123
Range11.352626
Interquartile range (IQR)1.1573696

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)nan
Kurtosis4.1300209
Mean0
Median Absolute Deviation (MAD)0.57218274
Skewness1.1430278
Sum-3.6082248 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:15.286058image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1076741458 223
 
5.2%
-0.2117073714 36
 
0.8%
-0.1596907586 36
 
0.8%
-0.3287447502 34
 
0.8%
-0.3677572098 32
 
0.7%
-0.06866168618 31
 
0.7%
-0.02964922657 31
 
0.7%
-0.1726949118 30
 
0.7%
-0.250719831 30
 
0.7%
-0.2897322906 30
 
0.7%
Other values (399) 3773
88.0%
ValueCountFrequency (%)
-2.422413416 1
< 0.1%
-2.292371884 1
< 0.1%
-2.266363577 1
< 0.1%
-2.201342811 1
< 0.1%
-2.162330352 1
< 0.1%
-2.097309586 1
< 0.1%
-2.071301279 1
< 0.1%
-2.045292973 1
< 0.1%
-2.03228882 1
< 0.1%
-2.006280513 1
< 0.1%
ValueCountFrequency (%)
8.93021233 1
< 0.1%
8.201979751 1
< 0.1%
6.381398302 1
< 0.1%
5.588144957 1
< 0.1%
4.924933144 1
< 0.1%
4.66485008 1
< 0.1%
4.612833467 1
< 0.1%
4.469787782 1
< 0.1%
4.248717177 1
< 0.1%
4.196700564 1
< 0.1%

num__feat01
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4655079 × 10-17
Minimum-3.577363
Maximum3.5060322
Zeros0
Zeros (%)0.0%
Negative2136
Negative (%)49.8%
Memory size67.0 KiB
2024-03-01T16:15:15.359979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-3.577363
5-th percentile-1.6281226
Q1-0.68358223
median0.0036687721
Q30.68141327
95-th percentile1.6149123
Maximum3.5060322
Range7.0833951
Interquartile range (IQR)1.3649955

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)1.5468494 × 1016
Kurtosis0.041123677
Mean6.4655079 × 10-17
Median Absolute Deviation (MAD)0.6831644
Skewness0.002313614
Sum2.1849189 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:15.439385image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1407397572 1
 
< 0.1%
-2.641139801 1
 
< 0.1%
-0.8719608256 1
 
< 0.1%
-0.1873261922 1
 
< 0.1%
-0.1737582648 1
 
< 0.1%
0.9024035484 1
 
< 0.1%
0.09586108346 1
 
< 0.1%
-1.196971997 1
 
< 0.1%
0.0187207222 1
 
< 0.1%
0.7768815386 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-3.577362989 1
< 0.1%
-3.279285608 1
< 0.1%
-3.195740334 1
< 0.1%
-3.101543274 1
< 0.1%
-3.065010838 1
< 0.1%
-3.030146359 1
< 0.1%
-2.947503235 1
< 0.1%
-2.916138784 1
< 0.1%
-2.882569936 1
< 0.1%
-2.828985072 1
< 0.1%
ValueCountFrequency (%)
3.506032161 1
< 0.1%
3.40552389 1
< 0.1%
3.286833044 1
< 0.1%
3.168484297 1
< 0.1%
3.119974074 1
< 0.1%
3.099794603 1
< 0.1%
3.068629505 1
< 0.1%
3.024293012 1
< 0.1%
3.022278149 1
< 0.1%
2.954628593 1
< 0.1%

num__feat02
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.6419031 × 10-17
Minimum-4.9404397
Maximum4.1052462
Zeros0
Zeros (%)0.0%
Negative2124
Negative (%)49.6%
Memory size67.0 KiB
2024-03-01T16:15:15.522406image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-4.9404397
5-th percentile-1.6172834
Q1-0.66725029
median0.014597702
Q30.66838006
95-th percentile1.6081116
Maximum4.1052462
Range9.0456859
Interquartile range (IQR)1.3356304

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)-2.1545402 × 1016
Kurtosis0.38880623
Mean-4.6419031 × 10-17
Median Absolute Deviation (MAD)0.66816149
Skewness-0.09180027
Sum-2.4358293 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:15.606810image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.381970415 1
 
< 0.1%
0.08788166889 1
 
< 0.1%
-0.5405516043 1
 
< 0.1%
-0.124579972 1
 
< 0.1%
-0.9549933268 1
 
< 0.1%
-0.7024756511 1
 
< 0.1%
-0.08272220739 1
 
< 0.1%
-0.2672739325 1
 
< 0.1%
-1.170578111 1
 
< 0.1%
0.5533569669 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-4.940439708 1
< 0.1%
-3.7057266 1
< 0.1%
-3.63397064 1
< 0.1%
-3.523802708 1
< 0.1%
-3.484433572 1
< 0.1%
-3.479921922 1
< 0.1%
-3.471195681 1
< 0.1%
-3.450923116 1
< 0.1%
-3.388939473 1
< 0.1%
-3.387265971 1
< 0.1%
ValueCountFrequency (%)
4.105246189 1
< 0.1%
4.070205781 1
< 0.1%
3.510437592 1
< 0.1%
3.457171004 1
< 0.1%
3.349250135 1
< 0.1%
3.293471232 1
< 0.1%
3.157044397 1
< 0.1%
3.121564763 1
< 0.1%
3.080409469 1
< 0.1%
3.042602445 1
< 0.1%

num__feat03
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4151144 × 10-16
Minimum-2.5196156
Maximum2.5926918
Zeros0
Zeros (%)0.0%
Negative2135
Negative (%)49.8%
Memory size67.0 KiB
2024-03-01T16:15:15.688973image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.5196156
5-th percentile-1.5833672
Q1-0.8042272
median0.0062112958
Q30.80801231
95-th percentile1.5722693
Maximum2.5926918
Range5.1123074
Interquartile range (IQR)1.6122395

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)2.9285012 × 1015
Kurtosis-0.88456815
Mean3.4151144 × 10-16
Median Absolute Deviation (MAD)0.80660816
Skewness-0.020920451
Sum1.4652723 × 10-12
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:15.772155image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.041548693 1
 
< 0.1%
1.172263899 1
 
< 0.1%
-0.8085048733 1
 
< 0.1%
-1.082645684 1
 
< 0.1%
1.507435631 1
 
< 0.1%
-0.2856690944 1
 
< 0.1%
0.8812798186 1
 
< 0.1%
1.333389454 1
 
< 0.1%
0.4759201951 1
 
< 0.1%
-2.122644594 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.519615572 1
< 0.1%
-2.51621234 1
< 0.1%
-2.48981891 1
< 0.1%
-2.447696814 1
< 0.1%
-2.438141237 1
< 0.1%
-2.416110818 1
< 0.1%
-2.369485919 1
< 0.1%
-2.328766522 1
< 0.1%
-2.312830311 1
< 0.1%
-2.29865481 1
< 0.1%
ValueCountFrequency (%)
2.592691799 1
< 0.1%
2.457098495 1
< 0.1%
2.369038919 1
< 0.1%
2.357052587 1
< 0.1%
2.353389951 1
< 0.1%
2.344052737 1
< 0.1%
2.326842972 1
< 0.1%
2.261895523 1
< 0.1%
2.248744544 1
< 0.1%
2.225189858 1
< 0.1%

num__feat04
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.1180239 × 10-17
Minimum-2.8429821
Maximum2.7453383
Zeros0
Zeros (%)0.0%
Negative2125
Negative (%)49.6%
Memory size67.0 KiB
2024-03-01T16:15:15.851107image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.8429821
5-th percentile-1.6123487
Q1-0.77639497
median0.015855662
Q30.77764935
95-th percentile1.6155657
Maximum2.7453383
Range5.5883204
Interquartile range (IQR)1.5540443

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)-1.0968568 × 1016
Kurtosis-0.7179262
Mean-9.1180239 × 10-17
Median Absolute Deviation (MAD)0.77513589
Skewness-0.0080774797
Sum-4.196643 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:15.935102image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6400589206 1
 
< 0.1%
0.9643662085 1
 
< 0.1%
0.1518450646 1
 
< 0.1%
0.3733282066 1
 
< 0.1%
-0.7952240461 1
 
< 0.1%
0.9009165985 1
 
< 0.1%
0.4378896421 1
 
< 0.1%
0.7223837029 1
 
< 0.1%
-1.305080334 1
 
< 0.1%
-0.4181957129 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.842982067 1
< 0.1%
-2.670889759 1
< 0.1%
-2.664504421 1
< 0.1%
-2.566929319 1
< 0.1%
-2.560926725 1
< 0.1%
-2.524223344 1
< 0.1%
-2.495329821 1
< 0.1%
-2.487255219 1
< 0.1%
-2.484356415 1
< 0.1%
-2.449435153 1
< 0.1%
ValueCountFrequency (%)
2.745338306 1
< 0.1%
2.684378988 1
< 0.1%
2.492298701 1
< 0.1%
2.468606418 1
< 0.1%
2.432827794 1
< 0.1%
2.406113292 1
< 0.1%
2.402477541 1
< 0.1%
2.395563171 1
< 0.1%
2.343607986 1
< 0.1%
2.340877797 1
< 0.1%

num__feat05
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6329552 × 10-16
Minimum-2.5591897
Maximum2.426359
Zeros0
Zeros (%)0.0%
Negative2147
Negative (%)50.1%
Memory size67.0 KiB
2024-03-01T16:15:16.022095image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.5591897
5-th percentile-1.5918171
Q1-0.76983587
median-0.0033447055
Q30.81696975
95-th percentile1.5796663
Maximum2.426359
Range4.9855487
Interquartile range (IQR)1.5868056

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)6.1245813 × 1015
Kurtosis-0.84785613
Mean1.6329552 × 10-16
Median Absolute Deviation (MAD)0.79088675
Skewness-0.025615278
Sum7.8348439 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:16.101450image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2671525299 1
 
< 0.1%
-0.1801778234 1
 
< 0.1%
0.2024583561 1
 
< 0.1%
2.007975076 1
 
< 0.1%
-0.2035166529 1
 
< 0.1%
1.730038925 1
 
< 0.1%
-0.04486956482 1
 
< 0.1%
1.057288455 1
 
< 0.1%
0.3243331312 1
 
< 0.1%
-1.417897277 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.559189657 1
< 0.1%
-2.529675729 1
< 0.1%
-2.522286794 1
< 0.1%
-2.5074993 1
< 0.1%
-2.394120751 1
< 0.1%
-2.379270639 1
< 0.1%
-2.372025769 1
< 0.1%
-2.328414302 1
< 0.1%
-2.311482521 1
< 0.1%
-2.284579373 1
< 0.1%
ValueCountFrequency (%)
2.426359002 1
< 0.1%
2.397539748 1
< 0.1%
2.355880703 1
< 0.1%
2.326239598 1
< 0.1%
2.306134009 1
< 0.1%
2.305186019 1
< 0.1%
2.287657706 1
< 0.1%
2.218500298 1
< 0.1%
2.20801233 1
< 0.1%
2.203380421 1
< 0.1%

num__feat06
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2191583 × 10-16
Minimum-2.6115542
Maximum2.6350777
Zeros0
Zeros (%)0.0%
Negative2171
Negative (%)50.7%
Memory size67.0 KiB
2024-03-01T16:15:16.182296image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.6115542
5-th percentile-1.5858295
Q1-0.77887891
median-0.01974201
Q30.79337417
95-th percentile1.6083579
Maximum2.6350777
Range5.2466319
Interquartile range (IQR)1.5722531

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)2.3704175 × 1015
Kurtosis-0.79781247
Mean4.2191583 × 10-16
Median Absolute Deviation (MAD)0.78045506
Skewness0.018384567
Sum1.9122377 × 10-12
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:16.263676image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6387420462 1
 
< 0.1%
-1.18311558 1
 
< 0.1%
1.807740648 1
 
< 0.1%
-1.138742448 1
 
< 0.1%
0.9009861697 1
 
< 0.1%
1.143640661 1
 
< 0.1%
0.5343760168 1
 
< 0.1%
-0.3471987444 1
 
< 0.1%
-1.357228536 1
 
< 0.1%
-0.839685017 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.611554187 1
< 0.1%
-2.579609941 1
< 0.1%
-2.504497518 1
< 0.1%
-2.488261536 1
< 0.1%
-2.434636564 1
< 0.1%
-2.348122541 1
< 0.1%
-2.319538536 1
< 0.1%
-2.304463953 1
< 0.1%
-2.30175415 1
< 0.1%
-2.294930499 1
< 0.1%
ValueCountFrequency (%)
2.635077727 1
< 0.1%
2.520235143 1
< 0.1%
2.501766843 1
< 0.1%
2.439938061 1
< 0.1%
2.43656593 1
< 0.1%
2.425548048 1
< 0.1%
2.403921907 1
< 0.1%
2.402133484 1
< 0.1%
2.307427651 1
< 0.1%
2.283474841 1
< 0.1%

num__feat07
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6259836 × 10-17
Minimum-2.6745483
Maximum2.6898954
Zeros0
Zeros (%)0.0%
Negative2121
Negative (%)49.5%
Memory size67.0 KiB
2024-03-01T16:15:16.349443image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.6745483
5-th percentile-1.5984741
Q1-0.79463398
median0.015295091
Q30.77163547
95-th percentile1.5836986
Maximum2.6898954
Range5.3644436
Interquartile range (IQR)1.5662695

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)1.3114593 × 1016
Kurtosis-0.78021848
Mean7.6259836 × 10-17
Median Absolute Deviation (MAD)0.78488344
Skewness-0.014239685
Sum3.2129854 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:16.436231image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.750597437 1
 
< 0.1%
0.2341826264 1
 
< 0.1%
0.68272752 1
 
< 0.1%
-0.02781112985 1
 
< 0.1%
-1.499648195 1
 
< 0.1%
-1.265609483 1
 
< 0.1%
0.7802810114 1
 
< 0.1%
-0.7167639114 1
 
< 0.1%
1.025678396 1
 
< 0.1%
0.9923810348 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.674548288 1
< 0.1%
-2.582763589 1
< 0.1%
-2.580410152 1
< 0.1%
-2.53545528 1
< 0.1%
-2.478984032 1
< 0.1%
-2.441832273 1
< 0.1%
-2.373281991 1
< 0.1%
-2.363371457 1
< 0.1%
-2.3449043 1
< 0.1%
-2.338288555 1
< 0.1%
ValueCountFrequency (%)
2.689895352 1
< 0.1%
2.517921215 1
< 0.1%
2.513551921 1
< 0.1%
2.488852422 1
< 0.1%
2.484954535 1
< 0.1%
2.372349675 1
< 0.1%
2.333212263 1
< 0.1%
2.310928148 1
< 0.1%
2.275445903 1
< 0.1%
2.264709103 1
< 0.1%

num__feat08
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9852425 × 10-16
Minimum-2.6680301
Maximum2.838553
Zeros0
Zeros (%)0.0%
Negative2176
Negative (%)50.8%
Memory size67.0 KiB
2024-03-01T16:15:16.512898image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.6680301
5-th percentile-1.5616234
Q1-0.79489866
median-0.026512682
Q30.79762244
95-th percentile1.6099555
Maximum2.838553
Range5.5065831
Interquartile range (IQR)1.5925211

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)-5.0377558 × 1015
Kurtosis-0.78502345
Mean-1.9852425 × 10-16
Median Absolute Deviation (MAD)0.79254681
Skewness0.045368567
Sum-9.6202907 × 10-13
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:16.596422image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.027856192 1
 
< 0.1%
-0.9670749742 1
 
< 0.1%
-1.168758528 1
 
< 0.1%
-1.118253493 1
 
< 0.1%
0.8533649949 1
 
< 0.1%
-0.9621485768 1
 
< 0.1%
-0.9310934625 1
 
< 0.1%
-0.7558356686 1
 
< 0.1%
0.26094635 1
 
< 0.1%
-0.5859149929 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.668030091 1
< 0.1%
-2.548221982 1
< 0.1%
-2.536482774 1
< 0.1%
-2.482969768 1
< 0.1%
-2.454461357 1
< 0.1%
-2.441430036 1
< 0.1%
-2.382440518 1
< 0.1%
-2.351777004 1
< 0.1%
-2.33218643 1
< 0.1%
-2.311455388 1
< 0.1%
ValueCountFrequency (%)
2.838552968 1
< 0.1%
2.784940575 1
< 0.1%
2.698708085 1
< 0.1%
2.661235078 1
< 0.1%
2.576404357 1
< 0.1%
2.533769534 1
< 0.1%
2.52554472 1
< 0.1%
2.490233927 1
< 0.1%
2.444844918 1
< 0.1%
2.405685602 1
< 0.1%

num__feat09
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3322233 × 10-16
Minimum-2.5472451
Maximum2.5823786
Zeros0
Zeros (%)0.0%
Negative2140
Negative (%)49.9%
Memory size67.0 KiB
2024-03-01T16:15:16.683007image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-2.5472451
5-th percentile-1.6025389
Q1-0.78909531
median0.0035233493
Q30.76606978
95-th percentile1.6050139
Maximum2.5823786
Range5.1296237
Interquartile range (IQR)1.5551651

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)3.0013495 × 1015
Kurtosis-0.80803407
Mean3.3322233 × 10-16
Median Absolute Deviation (MAD)0.77888752
Skewness0.0024430899
Sum1.3528068 × 10-12
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:16.769310image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02378616988 1
 
< 0.1%
0.8075499938 1
 
< 0.1%
1.035591947 1
 
< 0.1%
0.2259610067 1
 
< 0.1%
1.133452467 1
 
< 0.1%
-1.347727644 1
 
< 0.1%
2.004076467 1
 
< 0.1%
1.208106352 1
 
< 0.1%
-1.668693539 1
 
< 0.1%
-1.068867716 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-2.5472451 1
< 0.1%
-2.481806321 1
< 0.1%
-2.469796649 1
< 0.1%
-2.448423453 1
< 0.1%
-2.398565936 1
< 0.1%
-2.351515812 1
< 0.1%
-2.329117673 1
< 0.1%
-2.310314545 1
< 0.1%
-2.277684279 1
< 0.1%
-2.27416083 1
< 0.1%
ValueCountFrequency (%)
2.582378567 1
< 0.1%
2.519732467 1
< 0.1%
2.470942951 1
< 0.1%
2.39692384 1
< 0.1%
2.384646514 1
< 0.1%
2.355911242 1
< 0.1%
2.340850554 1
< 0.1%
2.339115061 1
< 0.1%
2.319816313 1
< 0.1%
2.311758083 1
< 0.1%

num__feat10
Real number (ℝ)

UNIQUE 

Distinct4286
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2233029 × 10-16
Minimum-3.7574658
Maximum3.4843637
Zeros0
Zeros (%)0.0%
Negative2119
Negative (%)49.4%
Memory size67.0 KiB
2024-03-01T16:15:16.850375image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-3.7574658
5-th percentile-1.6400339
Q1-0.66169266
median0.014234007
Q30.66395681
95-th percentile1.6519599
Maximum3.4843637
Range7.2418295
Interquartile range (IQR)1.3256495

Descriptive statistics

Standard deviation1.0001167
Coefficient of variation (CV)2.3680913 × 1015
Kurtosis-0.015493785
Mean4.2233029 × 10-16
Median Absolute Deviation (MAD)0.66199003
Skewness-0.02241118
Sum1.8688384 × 10-12
Variance1.0002334
MonotonicityNot monotonic
2024-03-01T16:15:16.937881image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4334897661 1
 
< 0.1%
0.3852790759 1
 
< 0.1%
0.1358097536 1
 
< 0.1%
-0.9222387321 1
 
< 0.1%
-0.09177928787 1
 
< 0.1%
1.205395868 1
 
< 0.1%
-1.067771338 1
 
< 0.1%
0.1214450014 1
 
< 0.1%
1.613109039 1
 
< 0.1%
-2.047254725 1
 
< 0.1%
Other values (4276) 4276
99.8%
ValueCountFrequency (%)
-3.757465822 1
< 0.1%
-3.109251881 1
< 0.1%
-3.067512032 1
< 0.1%
-3.034997482 1
< 0.1%
-3.031943278 1
< 0.1%
-2.976373169 1
< 0.1%
-2.942930354 1
< 0.1%
-2.924482394 1
< 0.1%
-2.90912044 1
< 0.1%
-2.879946951 1
< 0.1%
ValueCountFrequency (%)
3.484363708 1
< 0.1%
3.337541782 1
< 0.1%
3.140956663 1
< 0.1%
3.095650731 1
< 0.1%
3.048386245 1
< 0.1%
2.953034044 1
< 0.1%
2.906492353 1
< 0.1%
2.901351831 1
< 0.1%
2.849104858 1
< 0.1%
2.714150991 1
< 0.1%

num__heart_disease
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.1 KiB
-0.2567177515854906
4021 
3.895328600472671
 
265

Length

Max length19
Median length19
Mean length18.876342
Min length17

Characters and Unicode

Total characters80904
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.895328600472671
2nd row-0.2567177515854906
3rd row-0.2567177515854906
4th row-0.2567177515854906
5th row-0.2567177515854906

Common Values

ValueCountFrequency (%)
-0.2567177515854906 4021
93.8%
3.895328600472671 265
 
6.2%

Length

2024-03-01T16:15:17.022787image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.089655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2567177515854906 4021
93.8%
3.895328600472671 265
 
6.2%

Most occurring characters

ValueCountFrequency (%)
5 16349
20.2%
7 12593
15.6%
0 8572
10.6%
6 8572
10.6%
1 8307
10.3%
2 4551
 
5.6%
8 4551
 
5.6%
. 4286
 
5.3%
4 4286
 
5.3%
9 4286
 
5.3%
Other values (2) 4551
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72597
89.7%
Other Punctuation 4286
 
5.3%
Dash Punctuation 4021
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 16349
22.5%
7 12593
17.3%
0 8572
11.8%
6 8572
11.8%
1 8307
11.4%
2 4551
 
6.3%
8 4551
 
6.3%
4 4286
 
5.9%
9 4286
 
5.9%
3 530
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4021
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 16349
20.2%
7 12593
15.6%
0 8572
10.6%
6 8572
10.6%
1 8307
10.3%
2 4551
 
5.6%
8 4551
 
5.6%
. 4286
 
5.3%
4 4286
 
5.3%
9 4286
 
5.3%
Other values (2) 4551
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 16349
20.2%
7 12593
15.6%
0 8572
10.6%
6 8572
10.6%
1 8307
10.3%
2 4551
 
5.6%
8 4551
 
5.6%
. 4286
 
5.3%
4 4286
 
5.3%
9 4286
 
5.3%
Other values (2) 4551
 
5.6%

num__hypertension
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size354.9 KiB
-0.34589698335342406
3828 
2.8910341752770905
458 

Length

Max length20
Median length20
Mean length19.786281
Min length18

Characters and Unicode

Total characters84804
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.34589698335342406
2nd row-0.34589698335342406
3rd row-0.34589698335342406
4th row-0.34589698335342406
5th row2.8910341752770905

Common Values

ValueCountFrequency (%)
-0.34589698335342406 3828
89.3%
2.8910341752770905 458
 
10.7%

Length

2024-03-01T16:15:17.162763image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.229082image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.34589698335342406 3828
89.3%
2.8910341752770905 458
 
10.7%

Most occurring characters

ValueCountFrequency (%)
3 15770
18.6%
4 11942
14.1%
0 9030
10.6%
5 8572
10.1%
9 8572
10.1%
8 8114
9.6%
6 7656
9.0%
2 4744
 
5.6%
. 4286
 
5.1%
- 3828
 
4.5%
Other values (2) 2290
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76690
90.4%
Other Punctuation 4286
 
5.1%
Dash Punctuation 3828
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 15770
20.6%
4 11942
15.6%
0 9030
11.8%
5 8572
11.2%
9 8572
11.2%
8 8114
10.6%
6 7656
10.0%
2 4744
 
6.2%
7 1374
 
1.8%
1 916
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 15770
18.6%
4 11942
14.1%
0 9030
10.6%
5 8572
10.1%
9 8572
10.1%
8 8114
9.6%
6 7656
9.0%
2 4744
 
5.6%
. 4286
 
5.1%
- 3828
 
4.5%
Other values (2) 2290
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 15770
18.6%
4 11942
14.1%
0 9030
10.6%
5 8572
10.1%
9 8572
10.1%
8 8114
9.6%
6 7656
9.0%
2 4744
 
5.6%
. 4286
 
5.1%
- 3828
 
4.5%
Other values (2) 2290
 
2.7%

cat__ever_married_Yes
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
1.0
2852 
0.0
1434 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2852
66.5%
0.0 1434
33.5%

Length

2024-03-01T16:15:17.293664image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.351889image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2852
66.5%
0.0 1434
33.5%

Most occurring characters

ValueCountFrequency (%)
0 5720
44.5%
. 4286
33.3%
1 2852
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5720
66.7%
1 2852
33.3%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5720
44.5%
. 4286
33.3%
1 2852
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5720
44.5%
. 4286
33.3%
1 2852
22.2%

cat__gender_Male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
2502 
1.0
1784 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 2502
58.4%
1.0 1784
41.6%

Length

2024-03-01T16:15:17.412502image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.470958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2502
58.4%
1.0 1784
41.6%

Most occurring characters

ValueCountFrequency (%)
0 6788
52.8%
. 4286
33.3%
1 1784
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6788
79.2%
1 1784
 
20.8%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6788
52.8%
. 4286
33.3%
1 1784
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6788
52.8%
. 4286
33.3%
1 1784
 
13.9%

cat__residence_type_Urban
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
1.0
2201 
0.0
2085 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2201
51.4%
0.0 2085
48.6%

Length

2024-03-01T16:15:17.535332image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.592524image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2201
51.4%
0.0 2085
48.6%

Most occurring characters

ValueCountFrequency (%)
0 6371
49.5%
. 4286
33.3%
1 2201
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6371
74.3%
1 2201
 
25.7%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6371
49.5%
. 4286
33.3%
1 2201
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6371
49.5%
. 4286
33.3%
1 2201
 
17.1%

cat__smoking_status_formerly smoked
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
3517 
1.0
769 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3517
82.1%
1.0 769
 
17.9%

Length

2024-03-01T16:15:17.656283image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.712804image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3517
82.1%
1.0 769
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 7803
60.7%
. 4286
33.3%
1 769
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7803
91.0%
1 769
 
9.0%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7803
60.7%
. 4286
33.3%
1 769
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7803
60.7%
. 4286
33.3%
1 769
 
6.0%

cat__smoking_status_never smoked
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
2713 
1.0
1573 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2713
63.3%
1.0 1573
36.7%

Length

2024-03-01T16:15:17.776455image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.835635image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2713
63.3%
1.0 1573
36.7%

Most occurring characters

ValueCountFrequency (%)
0 6999
54.4%
. 4286
33.3%
1 1573
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6999
81.6%
1 1573
 
18.4%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6999
54.4%
. 4286
33.3%
1 1573
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6999
54.4%
. 4286
33.3%
1 1573
 
12.2%

cat__smoking_status_smokes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
3620 
1.0
666 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 3620
84.5%
1.0 666
 
15.5%

Length

2024-03-01T16:15:17.897590image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:17.955955image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3620
84.5%
1.0 666
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 7906
61.5%
. 4286
33.3%
1 666
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7906
92.2%
1 666
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7906
61.5%
. 4286
33.3%
1 666
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7906
61.5%
. 4286
33.3%
1 666
 
5.2%

cat__work_type_Never_worked
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
4271 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4271
99.7%
1.0 15
 
0.3%

Length

2024-03-01T16:15:18.018327image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:18.262177image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4271
99.7%
1.0 15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 8557
66.6%
. 4286
33.3%
1 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8557
99.8%
1 15
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8557
66.6%
. 4286
33.3%
1 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8557
66.6%
. 4286
33.3%
1 15
 
0.1%

cat__work_type_Private
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
1.0
2448 
0.0
1838 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2448
57.1%
0.0 1838
42.9%

Length

2024-03-01T16:15:18.323486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:18.381631image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2448
57.1%
0.0 1838
42.9%

Most occurring characters

ValueCountFrequency (%)
0 6124
47.6%
. 4286
33.3%
1 2448
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6124
71.4%
1 2448
 
28.6%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6124
47.6%
. 4286
33.3%
1 2448
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6124
47.6%
. 4286
33.3%
1 2448
 
19.0%

cat__work_type_Self-employed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
3587 
1.0
699 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3587
83.7%
1.0 699
 
16.3%

Length

2024-03-01T16:15:18.445146image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:18.505151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3587
83.7%
1.0 699
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 7873
61.2%
. 4286
33.3%
1 699
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7873
91.8%
1 699
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7873
61.2%
. 4286
33.3%
1 699
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7873
61.2%
. 4286
33.3%
1 699
 
5.4%

cat__work_type_children
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.6 KiB
0.0
3725 
1.0
561 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3725
86.9%
1.0 561
 
13.1%

Length

2024-03-01T16:15:18.566306image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:15:18.624198image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3725
86.9%
1.0 561
 
13.1%

Most occurring characters

ValueCountFrequency (%)
0 8011
62.3%
. 4286
33.3%
1 561
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8572
66.7%
Other Punctuation 4286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8011
93.5%
1 561
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 4286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8011
62.3%
. 4286
33.3%
1 561
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8011
62.3%
. 4286
33.3%
1 561
 
4.4%

Interactions

2024-03-01T16:15:13.528828image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.436986image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.211210image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.940528image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.846659image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.595535image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.313176image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.028561image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.913380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.632475image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.334886image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.055047image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.776476image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.597700image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.497918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.268095image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.000205image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.910129image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.652484image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.374837image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.088962image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.971837image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.693142image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.393521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.114254image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.836456image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.813326image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.557073image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.321771image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.055405image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.965122image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.703217image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.426477image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.144633image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.030277image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.746549image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.448683image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.166029image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.891532image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.869780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.616182image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.378897image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.110259image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.022266image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.760043image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.482876image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.213497image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.082478image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.802713image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.501940image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.224230image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.949125image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.928409image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.677976image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.438787image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.170724image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.080756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.822491image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.542036image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.270853image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.140806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.858756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.559611image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.280347image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.010457image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.985598image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.737069image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.492347image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.223583image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.137136image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.874840image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.592154image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.330510image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.193390image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.910900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.612225image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.337675image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.064149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.043326image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.795720image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.547274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.277952image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.192528image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.928326image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.643142image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.382663image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.245808image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.962088image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.664344image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.391269image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.127925image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.103963image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.856576image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.606186image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.335862image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.251968image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.983636image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.701083image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.439364image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.299723image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.015921image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.719916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.450884image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.186037image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.158713image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.911900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.661060image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.390182image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.307172image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.032363image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.754062image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.493139image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.351943image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.066381image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.777761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.505847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.245046image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.216371image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:04.971033image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.716023image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.443547image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.362526image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.088137image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.811584image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.553107image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.400758image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.116295image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.844692image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.558687image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.297025image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.273231image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.028242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.768798image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.498963image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.422153image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.142823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.865207image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.608705image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.452483image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.165861image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.896005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.613596image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.352183image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.331336image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.087405image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.824790image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.553766image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.481367image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.197693image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.920358image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.666429image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.506234image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.225517image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.946895image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.667217image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.408960image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:14.389655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.146687image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:05.879925image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:06.605064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:07.534922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.252537image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:08.971149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:09.720973image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:10.572623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.277666image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:11.998675image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:12.717828image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-01T16:15:13.465915image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-03-01T16:15:18.688642image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
cat__ever_married_Yescat__gender_Malecat__residence_type_Urbancat__smoking_status_formerly smokedcat__smoking_status_never smokedcat__smoking_status_smokescat__work_type_Never_workedcat__work_type_Privatecat__work_type_Self-employedcat__work_type_childrennum__agenum__avg_glucose_levelnum__bminum__feat01num__feat02num__feat03num__feat04num__feat05num__feat06num__feat07num__feat08num__feat09num__feat10num__heart_diseasenum__hypertension
cat__ever_married_Yes1.0000.0220.0000.1780.0900.1040.0780.1550.1900.5460.6420.1060.3710.012-0.006-0.005-0.013-0.019-0.011-0.0040.001-0.008-0.0410.1060.154
cat__gender_Male0.0221.0000.0040.0400.1010.0050.0000.0140.0100.081-0.0130.065-0.002-0.003-0.000-0.0030.005-0.000-0.005-0.0080.004-0.015-0.0070.0910.015
cat__residence_type_Urban0.0000.0041.0000.0000.0190.0220.0000.0000.0000.0000.0240.0020.0060.027-0.0090.010-0.0110.003-0.0050.017-0.0200.0060.0220.0000.000
cat__smoking_status_formerly smoked0.1780.0400.0001.0000.3550.1990.0170.0200.0840.1620.2380.0410.1200.0190.0060.0020.004-0.007-0.014-0.0060.008-0.023-0.0150.0610.052
cat__smoking_status_never smoked0.0900.1010.0190.3551.0000.3260.0380.1090.0200.2400.1040.0010.106-0.002-0.008-0.0110.0030.0100.002-0.023-0.000-0.0020.0010.0250.071
cat__smoking_status_smokes0.1040.0050.0220.1990.3261.0000.0130.0970.0000.1630.0510.0190.1000.0010.007-0.0110.012-0.0170.0060.013-0.0170.015-0.0010.0520.008
cat__work_type_Never_worked0.0780.0000.0000.0170.0380.0131.0000.0630.0140.008-0.073-0.014-0.036-0.011-0.0040.007-0.008-0.004-0.008-0.015-0.0080.0230.0230.0000.000
cat__work_type_Private0.1550.0140.0000.0200.1090.0970.0631.0000.5090.4470.0810.0180.2010.0370.005-0.0100.042-0.022-0.001-0.026-0.0110.022-0.0200.0000.000
cat__work_type_Self-employed0.1900.0100.0000.0840.0200.0000.0140.5091.0000.1700.3290.0250.086-0.009-0.007-0.006-0.0080.009-0.0040.0070.016-0.027-0.0180.0810.121
cat__work_type_children0.5460.0810.0000.1620.2400.1630.0080.4470.1701.000-0.583-0.069-0.468-0.0110.013-0.004-0.0210.006-0.0060.023-0.010-0.0020.0220.0940.132
num__age0.642-0.0130.0240.2380.1040.051-0.0730.0810.329-0.5831.0000.1590.3460.045-0.0310.0130.000-0.006-0.0060.0060.014-0.019-0.0600.3050.294
num__avg_glucose_level0.1060.0650.0020.0410.0010.019-0.0140.0180.025-0.0690.1591.0000.1350.003-0.014-0.0060.002-0.019-0.0090.0120.016-0.002-0.0290.1900.199
num__bmi0.371-0.0020.0060.1200.1060.100-0.0360.2010.086-0.4680.3460.1351.000-0.0090.0020.0200.009-0.0310.001-0.0000.0080.002-0.0090.1120.171
num__feat010.012-0.0030.0270.019-0.0020.001-0.0110.037-0.009-0.0110.0450.003-0.0091.0000.003-0.022-0.001-0.0140.008-0.0050.0020.008-0.0120.0370.040
num__feat02-0.006-0.000-0.0090.006-0.0080.007-0.0040.005-0.0070.013-0.031-0.0140.0020.0031.0000.0050.0210.019-0.010-0.0050.0010.0000.0130.0150.079
num__feat03-0.005-0.0030.0100.002-0.011-0.0110.007-0.010-0.006-0.0040.013-0.0060.020-0.0220.0051.0000.0050.003-0.0040.003-0.044-0.0100.0360.0360.000
num__feat04-0.0130.005-0.0110.0040.0030.012-0.0080.042-0.008-0.0210.0000.0020.009-0.0010.0210.0051.0000.0000.009-0.0190.0350.0130.0140.0000.000
num__feat05-0.019-0.0000.003-0.0070.010-0.017-0.004-0.0220.0090.006-0.006-0.019-0.031-0.0140.0190.0030.0001.0000.020-0.0150.004-0.0080.0230.0500.000
num__feat06-0.011-0.005-0.005-0.0140.0020.006-0.008-0.001-0.004-0.006-0.006-0.0090.0010.008-0.010-0.0040.0090.0201.0000.0370.011-0.015-0.0110.0210.000
num__feat07-0.004-0.0080.017-0.006-0.0230.013-0.015-0.0260.0070.0230.0060.012-0.000-0.005-0.0050.003-0.019-0.0150.0371.000-0.0080.009-0.0280.0550.016
num__feat080.0010.004-0.0200.008-0.000-0.017-0.008-0.0110.016-0.0100.0140.0160.0080.0020.001-0.0440.0350.0040.011-0.0081.0000.015-0.0270.0000.015
num__feat09-0.008-0.0150.006-0.023-0.0020.0150.0230.022-0.027-0.002-0.019-0.0020.0020.0080.000-0.0100.013-0.008-0.0150.0090.0151.000-0.0070.0000.000
num__feat10-0.041-0.0070.022-0.0150.001-0.0010.023-0.020-0.0180.022-0.060-0.029-0.009-0.0120.0130.0360.0140.023-0.011-0.028-0.027-0.0071.0000.0000.036
num__heart_disease0.1060.0910.0000.0610.0250.0520.0000.0000.0810.0940.3050.1900.1120.0370.0150.0360.0000.0500.0210.0550.0000.0000.0001.0000.103
num__hypertension0.1540.0150.0000.0520.0710.0080.0000.0000.1210.1320.2940.1990.1710.0400.0790.0000.0000.0000.0000.0160.0150.0000.0360.1031.000

Missing values

2024-03-01T16:15:14.500891image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-01T16:15:14.705496image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

num__agenum__avg_glucose_levelnum__bminum__feat01num__feat02num__feat03num__feat04num__feat05num__feat06num__feat07num__feat08num__feat09num__feat10num__heart_diseasenum__hypertensioncat__ever_married_Yescat__gender_Malecat__residence_type_Urbancat__smoking_status_formerly smokedcat__smoking_status_never smokedcat__smoking_status_smokescat__work_type_Never_workedcat__work_type_Privatecat__work_type_Self-employedcat__work_type_children
19601.1989-0.4675-0.5108-0.14070.3820-1.04150.64010.2672-0.6387-1.7506-1.02790.0238-0.43353.8953-0.34591.00001.00001.00001.00000.00000.00000.00000.00001.00000.0000
11630.8065-0.3089-0.0166-1.36461.62370.8398-0.34630.35921.00260.69521.1168-0.38070.0078-0.2567-0.34591.00000.00000.00000.00001.00000.00000.00001.00000.00000.0000
38390.58851.35150.2304-0.3920-0.72071.3805-1.30531.6575-1.3061-1.09550.1495-0.56171.4940-0.2567-0.34590.00001.00000.00000.00000.00000.00000.00000.00001.00000.0000
30261.5042-0.3617-0.8359-1.7886-1.32470.46040.0978-1.13490.1601-1.1049-0.8174-1.99020.1505-0.2567-0.34591.00000.00000.00000.00001.00000.00000.00001.00000.00000.0000
50870.6757-0.4628-0.43281.0475-1.27400.5998-1.64900.2095-0.10360.25602.3680-0.32951.0201-0.25672.89101.00001.00001.00000.00000.00001.00000.00001.00000.00000.0000
4481-0.0219-0.32000.99770.6011-1.15940.8819-0.06710.06790.9347-1.4555-1.2956-0.3540-0.9922-0.2567-0.34590.00000.00000.00000.00001.00000.00000.00001.00000.00000.0000
46271.4606-0.36900.71160.68770.5333-0.9865-0.60831.2520-0.04160.0546-1.4035-0.9966-0.0324-0.2567-0.34591.00001.00001.00001.00000.00000.00000.00001.00000.00000.0000
42751.5914-0.6197-1.2390-0.5998-2.4205-0.5506-0.1927-0.7699-0.1785-0.04860.66300.1906-0.51993.8953-0.34591.00000.00000.00000.00000.00000.00000.00000.00001.00000.0000
17371.1553-0.74280.43850.75390.20950.20080.3675-0.63551.35820.01380.6178-0.0612-0.42933.8953-0.34591.00001.00000.00001.00000.00000.00000.00000.00000.00000.0000
976-1.8532-0.5038-1.77222.0675-0.5308-1.3595-0.6023-0.75571.30841.2330-1.0544-1.9064-0.0628-0.2567-0.34590.00000.00001.00000.00000.00000.00000.00000.00000.00001.0000
num__agenum__avg_glucose_levelnum__bminum__feat01num__feat02num__feat03num__feat04num__feat05num__feat06num__feat07num__feat08num__feat09num__feat10num__heart_diseasenum__hypertensioncat__ever_married_Yescat__gender_Malecat__residence_type_Urbancat__smoking_status_formerly smokedcat__smoking_status_never smokedcat__smoking_status_smokescat__work_type_Never_workedcat__work_type_Privatecat__work_type_Self-employedcat__work_type_children
52380.7629-0.30230.36051.04740.65081.5635-1.86221.3963-0.00860.92622.0427-0.18300.6688-0.2567-0.34590.00001.00001.00000.00001.00000.00000.00000.00000.00000.0000
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